The petroleum pipeline infrastructure in North America, estimated at three million miles of pipeline, was constructed over a period of eighty years. Many of the operating pipelines are now more than 50 years old. In recent years public concern has arisen as a result of several high profile pipeline incidents that have had significant consequences, including the loss of life. As a result there is increased emphasis on improving the management of pipeline integrity. This increased emphasis has taken the form of laws, regulations, and industry standards leading to improved pipeline company practices.
A key component of effective pipeline integrity management is the integration of information about the condition of a pipeline so that site-specific risk analysis can be carried out to prioritize inspection and repair. As part of the process, data from multiple sources using multiple coordinate systems need to be translated and correlated into a common frame of reference so that data features can be aligned for observation of coincident events.
Unfortunately, in many cases pipeline operators are overwhelmed by this data and cannot effectively access, integrate, or analyze data relationships, thus limiting the value of this data in decision-making processes. There is thus a need to provide the pipeline industry with an effective and affordable way to meet these regulatory and operating challenges, and in particular, a need to effectively manage the vast amounts of collected data relating to pipeline infrastructure, in a way that enables pipeline operators to maintain pipeline integrity.
The traditional spatial integration technology, known as a Geographic Information System (GIS), was developed to function as a map-based interface for planning and marketing applications, but the known uses of GIS technologies for the integrity management of pipeline networks have been of limited success. Data management solutions based solely on a GIS require pipeline surveys without explicit positional information to be converted into a common linear reference system (typically chainage or stationing) such that disparate data sets may be overlaid and compared. This conversion, or spatial normalization, process is where much of the data management effort is spent and is often prone to error introduction. Even when small errors are introduced, the normalization process is often performed such that it is not auditable. If the underlying spatial errors are not reported, addressed, and understood, the value of the data integration and any subsequent analysis of the combined data set is questionable.